Comparison
ClickHouse vs DuckDB
| ClickHouse | DuckDB | |
|---|---|---|
| Stars | 50,197 | 41,869 |
| License | 📜 Apache-2.0 | 📜 MIT |
| Status | Active | Active |
| Momentum | Not enough data yet | Not enough data yet |
| Category | database, analytics | database, analytics |
ClickHouse
Pros
- Proven at very large scale — in production at companies including Uber, eBay, and Comcast for analytics and reporting workloads
- Column-oriented storage with strong compression, which keeps both storage costs and scan times down on analytical workloads
- Runs as a single node for smaller workloads just as well as it does as a cluster
Cons
- A server to deploy and operate (or a managed/cloud offering to pay for), not an embeddable library — more operational overhead than a single-file database
- Transactional, multi-row-update workloads aren't what it's built for; it's optimized for append-heavy analytical data, not OLTP
- Tuning a cluster (sharding, replication, resource limits) for production has a real learning curve
DuckDB
Pros
- Genuinely zero-dependency: builds and runs with just a C++17 compiler, no external services
- Strong SQL support, including window functions and complex joins, not a stripped-down dialect
- Can run entirely in the browser via WebAssembly for client-side analytics
Cons
- Single-machine by design — there's no clustering story for datasets or workloads that outgrow one process
- Built for analytical (read-heavy, aggregation-heavy) queries, not as a general-purpose transactional database for an application's primary datastore
- Multi-user concurrent write access isn't its design center the way a client-server database's is
How they differ
Both are open-source, column-oriented databases built for analytical (OLAP) SQL queries, and each already lists the other as an open-source alternative — but they sit at opposite ends of the deployment spectrum, and the real choice between them is about scale and operational shape, not raw query performance.
ClickHouse is a distributed, client-server database: you run it as a service (a single node, or a cluster of many), and clients connect to it over the network. That gives it a horizontal scaling story — add nodes as data volume or concurrent query load grows — and a track record handling very large, continuously-ingested datasets (event streams, logs, metrics) for many simultaneous users. The cost is operational: a server (or cluster) to deploy, monitor, and tune.
DuckDB is an in-process, embedded database — it runs inside your own application, script, or notebook, the same deployment model SQLite popularized, with no separate server at all. That makes it fast to adopt for local or single-machine analytics (a dataset on disk, a data-science notebook, a desktop app), but it's fundamentally single-machine: there's no clustering story for a workload that outgrows one process.
In short: reach for ClickHouse when the workload needs to scale across machines, serve many concurrent users, or ingest a continuous high-volume stream; reach for DuckDB when the data fits on one machine and you'd rather not run a server at all.